Publicado:
2024-10-03Número:
Vol. 18 Núm. 2 (2024)Sección:
Visión InvestigadoraApproach to the diagnosis of cesarean delivery using bio- inspired models
Aproximación al diagnóstico del parto por cesárea mediante modelos bio-inspirados
Palabras clave:
Artificial Intelligence, Bio-Inspired, Cesarean Section, Classification, Electrohystereography (EHG) (en).Palabras clave:
Inteligencia Artificial, Bio-Inspirado, Cesárea, Clasificación, Electrohistereografía (EHG) (es).Descargas
Resumen (en)
In 2021, the cesarean section-related maternal mortality rate in Colombia was 46.4%. Efforts to reduce this rate have focused on monitoring maternal health, but the high volume of data and patient load complicate comprehensive symptom tracking. This study introduces a bio- inspired model for classifying cesarean deliveries using demographic information and electrohystereographic (EHG) biosignals from the mother-child dyad. The implemented classifiers include K-nearest neighbors (KNN), multilayer perceptron (MLP), support vector machines (SVM), and deep learning algorithms.
For demographic data analysis, KNN achieved a sensitivity (S) of 100% and a specificity (ES) exceeding 80%, while SVM recorded an S of 75% and an ES of 83.3%. In EHG analysis, MLP demonstrated an S of 82.3% and an ES of 85.7%, followed by deep learning with an S of 72.8%. This model facilitates early detection of cesarean births by integrating maternal history and fetal behavior data.
Resumen (es)
La tasa de mortalidad materna por cesárea de Colombia en 2021 es 46,4 %, para disminuirlo, profesionales de la salud dan seguimiento a las gestantes, sin embargo, la densidad de información y el volumen de pacientes hace complejo tener en cuenta la sintomatología. Este artículo desarrolla un modelo bio-inspirado para la clasificación de parto por cesárea, basado en información demográfica y la bioseñal Electrohistereograma (EHG) del binomio madre-hijo. Los clasificadores son k vecinos más cercanos (KNN), perceptrón multicapa (MLP), máquinas de vectores de soporte (SVM) y aprendizaje profundo. Finalmente, se calcula el rendimiento.
El mejor desempeño del análisis de datos demográficos se obtiene con: KNN con sensibilidad (S) del 100 % y especificidad (ES) de más del 80 % junto a SVM con S del 75 % y ES del 83,3 %. El mayor rendimiento en el análisis del EHG son MLP con S de 82.3 % y ES de 85.7 %. Esta herramienta brinda apoyo a la detección temprana de nacimientos por cesárea, teniendo en cuenta los antecedentes de la gestante y el comportamiento del feto.
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